Introduces parser_defaults.json mapping model families to vLLM
tool_parser/reasoning_parser names, with longest-pattern-first matching.
The vllmDefaults hook auto-fills tool_parser and reasoning_parser
options at load time for known families, while the VLLMImporter writes
the same values into generated YAML so users can review and edit them.
Adds tests covering MatchParserDefaults, hook registration via
SetDefaults, and the user-override behavior.
Adds RegisterBackendHook/runBackendHooks so each backend can register
default-filling functions that run during ModelConfig.SetDefaults().
Migrates the existing GGUF guessing logic into hooks_llamacpp.go,
registered for both 'llama-cpp' and the empty backend (auto-detect).
Removes the old guesser.go shim.
The ToProto conversion was dropping tool_call_id and reasoning_content
even though both proto and Go fields existed, breaking multi-turn tool
calling and reasoning passthrough to backends.
* feat: add PreferDevelopmentBackends setting, expose isMeta/isDevelopment in API
- Add PreferDevelopmentBackends config field, CLI flag, runtime setting
- Add IsDevelopment() method to GalleryBackend
- Use AvailableBackendsUnfiltered in UI API to show all backends
- Expose isMeta, isDevelopment, preferDevelopmentBackends in backend API response
* feat: upgrade banner with Upgrade All button, detect pre-existing backends
- Add upgrade banner on Backends page showing count and Upgrade All button
- Fix upgrade detection for backends installed before version tracking:
flag as upgradeable when gallery has a version but installed has none
- Fix OCI digest check to flag backends with no stored digest as upgradeable
* feat: add backend versioning data model foundation
Add Version, URI, and Digest fields to BackendMetadata for tracking
installed backend versions and enabling upgrade detection. Add Version
field to GalleryBackend. Add UpgradeAvailable/AvailableVersion fields
to SystemBackend. Implement GetImageDigest() for lightweight OCI digest
lookups via remote.Head. Record version, URI, and digest at install time
in InstallBackend() and propagate version through meta backends.
* feat: add backend upgrade detection and execution logic
Add CheckBackendUpgrades() to compare installed backend versions/digests
against gallery entries, and UpgradeBackend() to perform atomic upgrades
with backup-based rollback on failure. Includes Agent A's data model
changes (Version/URI/Digest fields, GetImageDigest).
* feat: add AutoUpgradeBackends config and runtime settings
Add configuration and runtime settings for backend auto-upgrade:
- RuntimeSettings field for dynamic config via API/JSON
- ApplicationConfig field, option func, and roundtrip conversion
- CLI flag with LOCALAI_AUTO_UPGRADE_BACKENDS env var
- Config file watcher support for runtime_settings.json
- Tests for ToRuntimeSettings, ApplyRuntimeSettings, and roundtrip
* feat(ui): add backend version display and upgrade support
- Add upgrade check/trigger API endpoints to config and api module
- Backends page: version badge, upgrade indicator, upgrade button
- Manage page: version in metadata, context-aware upgrade/reinstall button
- Settings page: auto-upgrade backends toggle
* feat: add upgrade checker service, API endpoints, and CLI command
- UpgradeChecker background service: checks every 6h, auto-upgrades when enabled
- API endpoints: GET /backends/upgrades, POST /backends/upgrades/check, POST /backends/upgrade/:name
- CLI: `localai backends upgrade` command, version display in `backends list`
- BackendManager interface: add UpgradeBackend and CheckUpgrades methods
- Wire upgrade op through GalleryService backend handler
- Distributed mode: fan-out upgrade to worker nodes via NATS
* fix: use advisory lock for upgrade checker in distributed mode
In distributed mode with multiple frontend instances, use PostgreSQL
advisory lock (KeyBackendUpgradeCheck) so only one instance runs
periodic upgrade checks and auto-upgrades. Prevents duplicate
upgrade operations across replicas.
Standalone mode is unchanged (simple ticker loop).
* test: add e2e tests for backend upgrade API
- Test GET /api/backends/upgrades returns 200 (even with no upgrade checker)
- Test POST /api/backends/upgrade/:name accepts request and returns job ID
- Test full upgrade flow: trigger upgrade via API, wait for job completion,
verify run.sh updated to v2 and metadata.json has version 2.0.0
- Test POST /api/backends/upgrades/check returns 200
- Fix nil check for applicationInstance in upgrade API routes
* feat: add toggle mechanism to enable/disable models from loading on demand
Implements #9303 - Adds ability to disable models from being auto-loaded
while keeping them in the collection.
Backend changes:
- Add Disabled field to ModelConfig struct with IsDisabled() getter
- New ToggleModelEndpoint handler (PUT /models/toggle/:name/:action)
- Request middleware returns 403 when disabled model is requested
- Capabilities endpoint exposes disabled status
Frontend changes:
- Toggle switch in System > Models table Actions column
- Visual indicators: dimmed row, red Disabled badge, muted icons
- Tooltip describes toggle function on hover
- Loading state while API call is in progress
* fix: remove extra closing brace causing syntax error in request middleware
* refactor: reorder Actions column - Stop button before toggle switch
* refactor: migrate from toggle to toggle-state per PR review feedback
When TASK_RESPONSE_TYPE_OAI_CHAT is used, the first streaming token
produces a JSON array with two elements: a role-init chunk and the
actual content chunk. The grpc-server loop called attach_chat_deltas
for both elements with the same raw_result pointer, stamping the first
token's ChatDelta.Content on both replies. The Go side accumulated both,
emitting the first content token twice to SSE clients.
Fix: in the array iteration loops in PredictStream, detect role-init
elements (delta has "role" key) and skip attach_chat_deltas for them.
Only content/reasoning elements get chat deltas attached.
Reasoning models are unaffected because their first token goes into
reasoning_content, not content.
The Go-side incremental JSON parser was emitting the same tool call on
every streaming token because it lacked the len > lastEmittedCount guard
that the XML parser had. On top of that, the post-streaming default:
case re-emitted all tool calls from index 0, duplicating everything.
This produced duplicate delta.tool_calls events causing clients to
accumulate arguments as "{args}{args}" — invalid JSON.
Fixes:
- JSON incremental parser: add len(jsonResults) > lastEmittedCount guard
and loop from lastEmittedCount (matching the XML parser pattern)
- Post-streaming default: case: skip i < lastEmittedCount entries that
were already emitted during streaming
- JSON parser: use blocking channel send (matching XML parser behavior)
When clients like Nextcloud or Home Assistant send requests with tools
to thinking models (e.g. Gemma 4 with <|channel>thought tags), the
response was empty despite the backend producing valid content.
Root cause: the C++ autoparser puts clean content in both the raw
Response and ChatDeltas. The Go-side PrependThinkingTokenIfNeeded
then prepends the thinking start token to the already-clean content,
causing ExtractReasoning to classify the entire response as unclosed
reasoning. This made cbRawResult empty, triggering a retry loop that
never succeeds.
Two fixes:
- inference.go: check ChatDeltas for content/tool_calls regardless of
whether Response is empty, so skipCallerRetry fires correctly
- chat.go: when ChatDeltas have content but no tool calls, use that
content directly instead of falling back to the empty cbRawResult
This changeset makes visible when files are being staged, so users are
aware that the model "isn't ready yet" for requests.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(ui): Add dynamic model editor with autocomplete
Signed-off-by: Richard Palethorpe <io@richiejp.com>
* chore(docs): Add link to longformat installation video
Signed-off-by: Richard Palethorpe <io@richiejp.com>
---------
Signed-off-by: Richard Palethorpe <io@richiejp.com>
We were not checking against the api keys when db == nil.
This commit also cleanups now unused middleware
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(chat): do not retry if we had chatdeltas or tooldeltas from backend
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: use oai compat for llama.cpp
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix: apply to non-streaming path too
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* map also other fields
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
The C++ PEG parser needs a few tokens to identify the reasoning format
(e.g. "<|channel>thought\n" for Gemma 4). During this warm-up, the gRPC
layer was sending raw partial tag tokens to Go, which leaked into the
reasoning field.
- Clear reply.message in gRPC when autoparser is active but has no diffs
yet, matching llama.cpp server behavior of only emitting classified output
- Prefer C++ autoparser chat deltas for reasoning/content in all streaming
paths, falling back to Go-side extraction for backends without autoparser
(e.g. vLLM)
- Override non-streaming no-tools result with chat delta content when available
- Guard PrependThinkingTokenIfNeeded against partial tag prefixes during
streaming accumulation
- Reorder default thinking tokens so <|channel>thought is checked before
<|think|> (Gemma 4 templates contain both)
* always enable parallel requests
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add node reconciler, allow to schedule to group of nodes, min/max autoscaler
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore: move tests to ginkgo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* chore(smart router): order by available vram
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat: add distributed mode (experimental)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix data races, mutexes, transactions
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix events and tool stream in agent chat
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* use ginkgo
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(cron): compute correctly time boundaries avoiding re-triggering
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not flood of healthy checks
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* do not list obvious backends as text backends
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* tests fixups
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* refactoring and consolidation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* Drop redundant healthcheck
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* enhancements, refactorings
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
The OpenAI Node.js SDK v4+ sends encoding_format=base64 by default.
LocalAI previously ignored this parameter and always returned a float
JSON array, causing a silent data corruption bug in any Node.js client
(AnythingLLM Desktop, LangChain.js, LlamaIndex.TS, …):
// What the client does when it expects base64 but receives a float array:
Buffer.from(floatArray, 'base64')
Node.js treats a non-string first argument as a byte array — each
float32 value is truncated to a single byte — and Float32Array then
reads those bytes as floats, yielding dims/4 values. Vector databases
(Qdrant, pgvector, …) then create collections with the wrong dimension,
causing all similarity searches to fail silently.
e.g. granite-embedding-107m (384 dims) → 96 stored in Qdrant
jina-embeddings-v3 (1024 dims) → 256 stored in Qdrant
Changes:
- core/schema/prediction.go: add EncodingFormat string field to
PredictionOptions so the request parameter is parsed and available
throughout the request pipeline
- core/schema/openai.go: add EmbeddingBase64 string field to Item;
add MarshalJSON so the "embedding" JSON key emits either []float32
or a base64 string depending on which field is populated — all other
Item consumers (image, video endpoints) are unaffected
- core/http/endpoints/openai/embeddings.go: add floatsToBase64()
which packs a float32 slice as little-endian bytes and base64-encodes
it; add embeddingItem() helper; both InputToken and InputStrings loops
now honour encoding_format=base64
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
